Environmental Health Perspectives Volume 105, Number 2, February 1997 

Research Article 

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Air Pollution and Emergency Room Visits for Asthma in Santa Clara 

County, California 

Michael Lipsett,1Susan Hurley,2and Bart Ostro1

1California Office of Environmental Health Hazard Assessment, Berkeley, California 94704 

USA; 2California Department of Health Services, Berkeley, California 94704 USA 

 Abstract

 Introduction

 Data and Methods

 Results

 Discussion

Abstract

During the winters of 1986-1987 through 1991-1992, rainfall throughout much of Northern 

California was subnormal, resulting in intermittent accumulation of air pollution, much of 

which was attributable to residential wood combustion (RWC) . This investigation 

examined whether there was a relationship between ambient air pollution in Santa Clara 

County, California and emergency room visits for asthma during the winters of 1988-1989 

through 1991-1992. Emergency room (ER) records from three acute-care hospitals were 

abstracted to compile daily visits for asthma and a control diagnosis (gastroenteritis) for 3-

month periods during each winter. Air monitoring data included daily coefficient of haze 

(COH) and every-other-day particulate matter with aerodynamic diameter equal to or less 

than 10 microns (PM10, 24-hr average) , as well as hourly nitrogen dioxide and ozone 

concentrations. Daily COH measurements were used to predict values for missing days of 

PM10 to develop a complete PM10 time series. Daily data were also obtained for 

temperature, precipitation, and relative humidity. In time-series analyses using Poisson 

regression, consistent relationships were found between ER visits for asthma and PM10. 

Same-day nitrogen dioxide concentrations were also associated with asthma ER visits, while 

ozone was not. Because there was a significant interaction between PM10 and minimum 

temperature in this data set, estimates of relative risks (RRs) for PM10-associated asthma ER 

visits were temperature-dependent. A 60 g/m3 change in PM10 (2-day lag) corresponded to 
RRs of 1.43 (95% CI = 1.18-1.69) at 20°F, representing the low end of the temperature 

distribution, 1.27 (95% CI = 1.13-1.42) at 30°F, and 1.11 (95% CI = 1.03-1.19) at 41°F, the 

mean of the observed minimum temperatures. ER visits for gastroenteritis were not 

significantly associated with any pollutant variable. Several sensitivity analyses, including 

the use of robust regressions and of nonparametric methods for fitting time trends and 

temperature effects in the data, supported these findings. These results demonstrate an 

association between ambient wintertime PM10 and exacerbations of asthma in an area where 

one of the principal sources of PM10 is RWC. Key words: asthma, emergency room 

visits, epidemiology, particulate air pollution, wood smoke. Environ Health 

Perspect 105:216-222 (1997)

Address correspondence to M. Lipsett, California Office of Environmental Health Hazard 

Assessment, California Environmental Protection Agency, 2151 Berkeley Way, Annex 11, 

Berkeley, CA 94704 USA. 

The authors would like to acknowledge the assistance of Constance Heye in abstracting the 

emergency room data, the staff of the Bay Area Air Quality Management District for 

providing the air monitoring data, David Fairley, Bay Area Air Quality Management 

District, and Lianne Sheppard, University of Washington, for helpful comments on earlier 

drafts. The contents and opinions expressed in this manuscript are those of the authors and 

do not represent the official position of the Office of Environmental Health Hazard 

Assessment, the California Environmental Protection Agency, or the State of California. 

This paper was presented in part at an International Specialty Conference on Particulate 

Matter, Pittsburgh, PA, sponsored by the Air & Waste Management Association, April 

1995, and at the annual meeting of the American Thoracic Society, Seattle WA, May 1995. 

Received 11 June 1996 ; accepted 13 November 1996. 

Introduction

Beginning with the winter of 1986-1987, Northern California experienced a 6-year drought 

during which many inland valleys were subject to intermittent air stagnation and decreased 

visibility. During this period, there were numerous anecdotal reports that smoke from 

residential wood combustion (RWC) was causing adverse respiratory effects. There are an 

estimated 1-1.5 million fireplaces and 300,000 wood-burning stoves in the San Francisco 

Bay Area (1). Source apportionment analysis sponsored by the Bay Area Air Quality 

Management District (BAAQMD) has shown that, on average, RWC accounts for 

approximately 45% of winter PM10 (particulate matter less than or equal to 10 m in 

aerodynamic diameter) in Santa Clara County, located at the southern end of the Bay Area 

(2). During the source apportionment sampling, RWC was the largest single identified 

source of winter PM10 in Santa Clara County, quantitatively approximating the sum of 

motor vehicle emissions and entrained road dust. Wood smoke contains many respiratory

irritants in addition to particles, including low molecular weight aldehydes and acids, 

nitrogen oxides, and sulfur dioxide (3). Indoor exposures to this complex mixture have 
previously been linked with increased risks of respiratory infection and otitis, increased 

symptoms of respiratory irritation, and exacerbations of asthma symptoms (4-8). Several 

studies undertaken in the Pacific Northwest suggest that ambient particles, many of which 

are attributable to RWC, are linked with decrements in children's lung function and 

increased hospital emergency room (ER) visits for asthma (9-11). This investigation was 

initiated to examine the relationship between ER visits for asthma and ambient air 

pollutant concentrations in Santa Clara County during the winters of 1988-1989 through 

1991-1992. 

Data and Methods

Visits for asthma to three acute-care facilities in Santa Clara County for the winters of 

1988-1989 through 1991-1992 (1 November-31 January) were abstracted from ER 

logbooks by one of the co-authors (Hurley) and an epidemiology graduate student. Visits 

for gastroenteritis, a control diagnosis considered unlikely to be related to air pollution, 

were also abstracted. Daily counts of ER visits for asthma and gastroenteritis were 

compiled for each hospital. Summed hospital-specific counts were used as the primary

dependent variable in the analysis, since preliminary analyses had indicated the presence of 

significant interhospital heterogeneity, possibly due to differences in diagnostic 

preferences and in the populations served (one facility was a county hospital, whereas the 

other two were private). Air monitoring data were obtained from the BAAQMD for the 

principal San Jose monitoring site, centrally located in the Santa Clara Valley. Particulate 

matter (PM) metrics included coefficient of haze (COH), a measure of light transmittance, 

which was recorded every 2 hr, and PM10, which was recorded as a 24-hr average with a 

high-volume sampler every other day. For one 45-day period during the 1991-1992 winter, 

however, PM10 was measured only every sixth day. Ozone (O3) and nitrogen dioxide 

(NO2) were measured continuously and reported by the BAAQMD as hourly averages. We 

regressed measured PM10 on corresponding daily average values of COH in order to 

predict missing PM10 values so that we could conduct analyses with a daily PM10 metric. 

The R2value for this linear predictive model for the winters of 1988-1989 through 1991-

1992 was 0.81. Meteor-ological data included daily temperature, relative humidity, and 

precipitation, which were obtained from the National Climatic Data Center in Asheville, 

North Carolina. The meteorological, air quality, and health data were entered and merged 

for analysis using PC-SAS (SAS Institute, Cary, NC), S-Plus (StatSci, Seattle, WA), and 

Stata (Stata Corp, College Station, TX) (12-14). 

The principal analysis relied on Poisson regression, which assumes that counts of 

independent, rare events follow a Poisson distribution, conditional on the explanatory

variables. However, heterogeneity among the asthmatics' exposures, respiratory infections, 

and other factors may result in overdispersion of the data in relation to a classic Poisson 

distribution. This in turn may affect the standard errors of the regression coefficients, 

leading to incorrect significance tests. To address this issue, the extra-Poisson variability

was modeled and incorporated into the estimates of the standard errors using PROC 

GENMOD in PC-SAS, which adopts the approach of McCullagh and Nelder (15). While 

the estimated coefficients remain the same as in Poisson regression, the standard errors are 

estimated by multiplying those obtained from the Poisson model by a dispersion 
parameter. 

Initially we ran cross-correlations of the various explanatory variables to examine whether 

multicollinearity would be a concern in subsequent analyses. Pollutant variables found to 

be strongly correlated (r>0.6) were not included together in the regression models. We then 

ran univariate regressions of asthma ER visits on each of the explanatory meteorological 

and pollutant variables. In general, covariates were examined in multivariate models if 

their t-statistics in the univariate regressions were equal to or greater than one. The PM 

variables were examined as daily average COH and PM10 (described above). Gaseous 

pollutants were modeled as daily peak 1-hr concentrations of O3 and NO2. The influence of 

temperature was examined by using daily minimum temperatures (including several lagged 

specifications) and by running models with locally weighted regression (loess) smooths of 

temperature (spans of 90 and 45 days) (see below). Percent relative humidity (measured 

daily at 4 p.m.) was examined as a continuous variable, with contemporaneous and lagged 

values of up to 4 previous days considered. Precipitation was modeled as a binary variable. 

To control for interhospital differences in ER utilization, the regression models also 

included indicator variables representing each hospital since, as noted above, preliminary

analysis indicated the presence of significant interhospital heterogeneity. Separate sets of 

indicator variables were also included to model short-term (i.e., day of week) and longterm (i.e., annual) trends in ER utilization. Several terms were used to model potential 

interactions between temperature and the pollutant variables. 

Numerous reports document a variety of lag structures relating ambient particle 

concentrations to both morbidity and daily mortality. Schwartz and colleagues recently

reported that the strongest associations of PM10 and asthma ER visits in Seattle were found 

when the explanatory variable was defined as the mean of the previous 4 days'

concentrations of PM10 (11). We investigated the effects of up to 5-day lags as well as 

multiday averages of PM10. 

Positive serial correlation is a common feature of time-series data, and can result in 

underestimation of the standard errors of regression coefficients. Examination of 

autocorrelation function (ACF) plots of the model residuals showed slight serial correlation 

only at lag 1 ( = 0.12-0.14 for the various models). This minimal degree of serial 

correlation is unlikely to produce biased significance tests (16). Nevertheless, to address 

this issue, we reran the models using the general estimating equations (GEE) of Liang and 

Zeger (17). In this approach, the covariance structure is incorporated into the estimation of 

the regression coefficients in addition to their variances, theoretically yielding robust 

estimators and correcting for serial correlation in the data. Though this method has been 

used in other time-series data sets (11), recent simulations suggest that when the data are 

structured in few independent blocks, the GEE model may overstate the significance of 

regression coefficients. Accordingly, when using the GEE, we structured the data into 12 

blocks (by hospital and year). Unlike Burnett and colleagues, we did not structure these 

blocks as random effects variables (18). 

Models that incorporate loess smooths of time also have been used successfully to reduce 

residual serial correlation in time-series data similar to these. When an explanatory
variable, smoothed in this fashion, is incorporated into a regression, the measured value of 

the variable is replaced with a locally weighted moving average and the regression is 

conducted on the moving average. Thus, adding a loess smooth of time diminishes shortterm fluctuations in the data, thereby helping to reduce the degree of residual serial 

correlation. This smoothing technique also allows for more parsimonious modeling of 

annual temporal trends in the data than the use of indicator variables in standard Poisson 

regressions. Furthermore, loess smoothing techniques can accommodate nonlinear 

patterns, offering a more flexible nonparametric modeling tool. Therefore, to control for 

temporal trends in the data and to allow for potential nonlinearities in the effect of 

temperature, we repeated the analysis using generalized additive models with loess 

smooths of time and temperature. Because our data represent discontinuous time series 

across years, we adjusted the span of the smooth to both 90 days (the length of one winter) 

and 45 days (half of the winter span). 

We undertook several additional sensitivity analyses, which included 1) conducting robust 

regressions (to minimize the effects of outliers and other potentially influential data points) 

using an iteratively reweighted least squares methodology; 2) incorporating several 

combinations of trigonometric terms to model and thereby reduce the impact of any longwave trends within the winter seasons that were not obvious by visual inspection of the 

data; and 3) fitting the same models to ER visits for gastroenteritis (the control diagnosis) 

as those used for asthma. 

Results

Descriptive statistics for the variables used in this investigation are displayed in Table 1. 

The mean PM10 concentration during the study period was 61.2 g/m3. During the 368 

observation-days of this investigation, there were 2 measured exceedances of the federal 

ambient air quality standard for PM10 (150 g/m3, 24-hr average), and 85 of the California 
standard (50 g/m3

, 24-hr average). Using COH readings to predict PM10 on days when it 

was not measured, however, resulted in estimates of 3 and 202 exceedances of the federal 

and state standards, respectively. O3 concentrations were generally at or near background 

levels throughout most of the observation period, as expected, since ground-level O3 is 

generated photochemically and attains elevated concentrations primarily from April to 

October in California. NO2 concentrations were substantially below the California 1-hr 

ambient air quality standard (0.25 ppm) during the entire study. On most days there was 

little or no precipitation. 

Table 2 is a correlation matrix of pollutant and meteorological variables. Minimum 

temperature was negatively associated with PM10, COH, and NO2, which would be 

expected to occur in the presence of the shallow thermal inversions common during 

Northern California winters. NO2 was strongly correlated with the particulate measures and 

thus was not used in any initial regressions with PM10 as an explanatory variable. 

In this paper, a relative risk (RR) reported for a pollutant variable represents the change in 

daily risk of seeking ER treatment for asthma associated with a given unit change in the 

ambient level of that pollutant. For PM10, we calculated RRs associated with a 60-unit 

change. Specifically, the RRs we report for PM10 represent ratios of the expected counts of 

asthma ER visits on days when 24-hr average ambient levels of PM10 were 60 g/m3

higher than on comparison days. While this is a moderately large pollution difference, it is 

well within the range of PM10 observed and is approximately equal to the mean PM10

levels for the study period. The initial estimates of the PM10-associated RRs for asthma ER 

visits were based on regressions that did not include a term for interaction between PM10

and minimum temperature. These RRs ranged from 1.52 to 1.79, depending on the PM10

metric used (i.e., unlagged and lagged pollutant variables as well as multiday averages), 

when calculated at the approximate mean concentration of PM10 (60 g/m3). The 3-day

average PM10 concentration attained statistical significance (RR = 1.73, 95% CI = 1.00-

2.97, p = 0.048); all other PM10 regression coefficients were of borderline significance (p = 

0.06). 

Figure 1. The joint influence of PM10 and minimum 

temperature on asthma emergency room visits, Santa Clara 

County, California, in the winters of 1988-1989 through 

1991-1992. 

Figure 1 is a plot of the joint influence of PM10 and minimum temperature on asthma ER 

visits. This graph suggests an interactive effect of PM10 and temperature primarily on days 

when the minimum temperature was less than the mean. Inclusion of an interaction term 

(PM X minimum temperature) increased the magnitude of the PM10 regression coefficients 

and resulted in statistical significance for all specifications of PM10, as displayed in Table 
3. The coefficient for the interaction term included in the regression models, though of 

small magnitude, was significant (p<0.05 in all but one model). In these models, the RR 

estimates for PM10 depend on the value of the interacting variable (i.e., minimum 

temperature). Therefore, we estimated RRs and confidence intervals using formulas that 

incorporated specific values for minimum daily temperature and the covariance of PM10

and the interaction term (19). 

The formulas for calculating RR given the interaction between PM10 and temperature took 

the following form: 

The results of these analyses are displayed in Table 3. As expected, the estimated RRs 

were strongly temperature-dependent. At the low end of the temperature distribution 

(20°F), the RRs ranged from 1.33 (3-day average) to 1.66 (1-day lag of PM10). At 30°F, the 

RRs for most of the specifications of PM10 were statistically significant, though the 

magnitudes of the RRs were less than those calculated at the lower temperature. At 41°F, 

the mean minimum daily temperature for the study period, only the 2-day lag of PM10

remained significant (RR = 1.11, 95% CI = 1.03-1.19). The GEE models produced 

somewhat lower estimates of the RRs, and none calculated at 41°F were significant. In 

contrast, the robust regressions resulted in somewhat higher estimates of PM10-associated 

RRs at 20°F and 30°F, but not at 41°F. Specification of a variety of trigonometric terms in 

the regression equations did not substantially affect the results. Table 4 compares the 

results from the Poisson, robust, GEE, and trigonometric model regressions for a 2-day

lagged specification of PM10. 
Models run with loess smooths of temperature and time (with spans of 90 or 45 days) did 

not markedly change the magnitude of the PM10 coefficients, though in several instances 

their significance decreased slightly. However, in these instances, coefficients for all 

various specifications of PM10 remained significant at p<0.05, except the 4-day lag. When 

the regressions were analyzed with temperature stratified into two levels (at the mean), the 

PM10 coefficients remained significant only for temperatures <41°F. Point estimates for 

RRs corresponding to a 60 g/m3change in PM10 (at 20°F), using only the days when 

T<41°F, ranged from 1.41 (4-day lag) to 2.17 (no lag), somewhat higher than the estimates 

derived using the entire data set. Insufficient statistical power precluded additional 

stratification on temperature. The addition of loess smooths of temperature and time to the 

regression models did not eliminate the small degree of autocorrelation at lag one. 

Other terms that were consistently associated with the outcome variable included the 

indicators for precipitation, hospital, day of week, day of study, and year. Relative 

humidity was not significantly related to ER visits for asthma in any of the initial models 

(p-values ranged from 0.5-0.7) and therefore was dropped from all subsequent runs. The 

models using COH as the particulate metric were not markedly different from those using 

PM10; in general, models with PM10 fit the data slightly better (data not shown). Using 

similar models with contemporaneous and lagged exposures, asthma ER visits were not 

significantly associated with O3 (e.g., for same-day ozone, using the full model, ßO3 = -

0.003 [p>0.29]). However, same-day NO2 was associated with ER visits for asthma (ßNO2 = 

0.013 [p = 0.024]). When included as an explanatory variable in regressions along with 

PM10, the NO2 coefficient became insignificant, while that for PM10 did not change in 

magnitude and remained statistically significant. In the models that included NO2 or O3

rather than PM10 as the pollutant variable, the temperature coefficient became highly 

significant (p<0.01). In models using unlagged, lagged, and multiday averages of pollutant 

variables, gastroenteritis ER visits were not significantly associated with PM10, COH, NO2

or O3 (data not shown). 

Discussion

In this investigation we found that a variety of specifications of PM10 were consistently 

associated with ER visits for asthma, but not for gastroenteritis. Several lagged 

specifications for PM10 provided modestly stronger associations with asthma ER visits than 

did same-day PM10. One explanation for this observation may lie in the pattern of exposure 

to winter particles in the Santa Clara Valley. Visual inspection of the 2-hr COH values and 

the results of more recent real-time monitoring data indicate that PM levels generally tend 

to increase markedly in the late afternoon and evening, a pattern that is consistent with 

RWC emissions. Peak levels, therefore, often occur in the evening, suggesting that if 

severe symptomatic reactions to exposure (manifested by a visit to the ER) were delayed 
by more than a few hours, they would not be observed until the following day or later. 

In Santa Clara County, the main source of winter particle concentrations is RWC, though 

motor vehicle exhaust and entrained road dust also make significant contributions (2). 

Wood smoke particles arise mainly from condensation of combustion gases and therefore 

tend to be distributed mainly in the submicron range, allowing substantial penetration to 

the indoor environment from outdoors (3). Wood smoke can also enter a residence directly

via backdrafting from a fireplace or wood stove: It is possible that some of the asthmatics 

who later sought care at the local ER were exposed to smoke emitted from wood-burning 

devices in their own homes. A recent report indicates that use of a fireplace or wood stove 

on a given day strongly predicts exacerbation of respiratory symptoms in adults who have 

moderate or severe asthma (8). However, because of the ecological design of this ER visit 

study, we could not assess individual exposures. 

Others have also found delayed asthmatic responses to particulate or smoke exposure 

(20,21). In an analysis of ER visits after the Berkeley-Oakland hills firestorm of 1991, 

Shusterman and colleagues found that the mean lag between exposure to smoke and a visit 

to the ER was between 1 and 2 days, which is also consistent with our findings [D. 

Shusterman, personal communication; (21)]. In Seattle, an area that has also experienced 

substantial wood smoke pollution, Schwartz and co-workers found that the mean PM10

concentration averaged over a 4-day period was the best predictor of ER asthma visits, also 

suggesting a delayed response (11). In a study of daily mortality in Santa Clara County in 

relation to particulate air pollution, Fairley found that the strongest association with the 

outcome was a 2-day lag (22). Although daily mortality is clearly not directly comparable 

to asthma ER visits, finding delayed particle-associated adverse health events in the same 

locale lends additional plausibility to each report. 

Our analysis also controlled for meteorological factors, and suggested that the combination 

of low temperature and particle concentrations is also an important predictor of asthma ER 

visits. Inclusion of the PM10 X minimum temperature interaction term increased both the 

magnitude and statistical significance of the PM10 coefficient, while the opposite was true 

for the coefficient for minimum temperature. In most model specifications the interaction 

term itself, though of small magnitude, was significant (p<0.05) and its inclusion improved 

the overall model fit. The significance of the PM X minimum temperature term in these 

regressions may be partly attributable to not having specified temperature adequately in 

other models. However, other specifications, including loess smooths of temperature, did 

not significantly improve the model fit without the interaction term. 

Interpretation of this statistical interaction in meaningful biological terms is somewhat 

speculative. While exposure to cold dry air can precipitate bronchospasm in asthmatics, 

temperature was not strongly correlated with asthma ER visits in models that also included 

PM10. However, in models where the pollutant variable was O3 or NO2, temperature was 

strongly associated with asthma ER visits. This difference may be attributable in part to the 

inverse correlation between PM10 and temperature (r -0.41 for measured PM10 and r -

0.48 for the full PM10 data set, including the days predicted from COH). During Bay Area 

winters, radiative inversions associated with low temperature frequently limit the vertical 
mixing depth to less than 100 ft, trapping pollutants near ground level. Moreover, people 

tend to use their fireplaces and wood stoves more often as the temperature drops. Thus, the 

association of asthma ER visits with higher PM10 concentrations in conjunction with colder 

days and nights may be partially explicable by both meteorology and human behavior. 

Decreasing ambient temperatures tend to be associated with increasing time spent inside, 

thus enhancing the likelihood of exposure to indoor allergens and pollutants (including 

backdrafted wood smoke). Acute exposures to several indoor sources of combustion (gas 

stoves, cigarette smoke, and wood-burning devices) have been reported to enhance the 

probability of an exacerbation of asthma in those with moderate to severe disease, which 

would include individuals likely to require intermittent urgent care (8). However, as noted 

earlier, the ecological design of this investigation precluded evaluation of individual 

exposures, which may confound or modify the relationships observed between ambient 

PM10 levels and asthma ER visits. 

The results are not likely to have been confounded by other measured pollutants or 

meteorological factors. Ozone has been associated with ER visits or increased symptoms 

of asthma in other settings, but in those situations the concentrations were substantially

higher than here (23-25). The strong correlation of NO2 with PM10 and COH probably 

reflects in part the contribution of motor vehicle emissions to winter particle loading. 

Nevertheless, NO2 was associated with the outcome only for same-day exposures. 

Although NO2 is a strong respiratory irritant, chamber studies of asthmatics suggest that 

the NO2 concentrations observed in this study would be unlikely to elicit a 

bronchoconstrictive response (26,27). However, participants in chamber studies do not 

represent the spectrum of disease in the general population and the short (usually 1- or 2-

hr) durations of the controlled studies cannot adequately capture the complexity of realworld exposures. Others have reported a relationship between ambient NO2 and ER visits 

for asthma and with pulmonary function changes in asthmatics (28,29). Nevertheless, the 

absence of an association between lagged or multiday specifications of NO2 and asthma 

ER visits in this data set, in addition to the observation that the NO2 regression coefficient 

lost its statistical significance in models that also included PM10, suggest that the same-day

association may be an artifact of covariation with PM10. This association, in turn, could 

also be due to the shallow thermal inversions in the Santa Clara Valley. 

Another possible explanation for the apparently stronger association of PM10 than NO2

with asthma ER visits may lie in the greater likelihood of exposure misclassification for the 

latter pollutant. High-temperature combustion results in the formation of NO2 and other 

nitrogen oxides, which tend to be elevated near streets and freeways with substantial traffic 

volume. Thus, a single NO2 monitoring site, as was used in this analysis, may not be 

representative of regional concentrations or personal exposures. In examining this issue, 

we ran pair-wise correlations among peak hourly NO2 concentrations at three regional 

fixed-site monitors, including the one used in this analysis, the coefficients of which 

ranged from 0.73 to 0.78 during the study period. Similar correlation coefficients (based on 

every-sixth-day sampling) among 24-hr averages for PM10 at the same sites ranged from 

0.90 to 0.92. Thus, even if NO2 exposure was causally related to serious asthma 

exacerbations, these results suggest that exposure misclassification alone could have 
resulted in an apparently stronger relationship of ER asthma visits with PM10 than with 

NO2, since such misclassification tends to bias the results towards the null hypothesis of no 

effect. 

Sulfur dioxide (SO2), another respiratory irritant to which asthmatics tend to be 

susceptible, was not measured by the BAAQMD during the study period because SO2

concentrations in prior years had been far below both the California and the federal 

ambient air quality standards. In 1988, for example, the last year for which SO2 was 

measured at this site, the peak 1-hr concentration was 4 ppb, whereas the annual average 

was 0.55 ppb. [For purposes of comparison, the California ambient air quality standard for 

SO2, which is intended to protect individuals with asthma, is 250 ppb (1-hr average)]. With 

such low SO2 concentrations, the sulfate fraction of PM10 also tends to be quite low 

compared with other regions in the United States. During the winters of 1988-1991, 

sulfates composed only about 4-5% of PM10 mass measured at the same monitoring site as 

that used in this analysis (30). In contrast, in urban areas on the East Coast, the comparable 

percentage is closer to 30% (31). 

Since some fungal spores and pollen fragments are within the size range encompassed by 

PM10, it is possible that the increased RRs reported here may have been confounded by 

exposure to aeroallergens, some of which are well recognized to bear a causal relationship 

to seasonal and epidemic asthma exacerbations. While theoretically possible, such 

confounding is unlikely to explain the associations observed here, where the PM10-

associated risk increased with decreasing temperature. Viral epidemics are also often cited 

as potential confounders of associations between air pollution and asthma or other 

respiratory conditions. We did not have data available to control for this potential

confounder. Moreover, to the extent that respiratory viral infections may constitute an 

intermediate stage on a causal pathway between exposure to PM10 and asthma 

exacerbations, it would be inappropriate to control for this variable in an analysis of 

potential air pollution effects (32). 

Our results indicate that a 10 g/m3increase in PM10 (2-day lag) would correspond to a 

relative risk of 1.06 (95% CI = 1.02-1.10) at 20°F, declining to 1.04 (95% CI = 1.02-1.06) 

at 30°F, and 1.02 (1.00-1.03) at the mean observed minimum temperature (41°F) using the 

Poisson model. These estimates were slightly lower using the GEE model (RR = 1.05; 95% 

CI = 1.02-1.08) and slightly higher using robust regressions (RR = 1.13; 95% CI = 1.03-

1.24). In Seattle, where RWC has also been a major source of winter particles, Schwartz 

and co-workers reported a RR of 1.12 for a 30 g/m

3

increase in PM10 for asthma ER 

visits, but their estimate was based on a multiday average of PM10 concentrations 

throughout the year (11). Using our data, a 30 g/m3change in PM10 (lag 2) would result 

in a RR of 1.20 (95% CI = 1.07-1.33) at 20°F, a RR of 1.13 (95% CI = 1.06-1.20) at 30°F, 

or 1.06 (95% CI = 1.02-1.10) at 41°F. Though Schwartz et al. did not report any PM10-

temperature interactions, their RR estimates are of comparable magnitude to ours. 

Whether these results could be replicated in seasons other than winter has not been 

examined. Given that the relationship between ER visits and PM10 concentrations was 

observed primarily at lower temperatures, it is possible that this association would not hold 
during other seasons. Moreover, though RWC takes place throughout the year in the Bay 

Area, overall PM10 levels and absolute concentrations of RWC-associated particles are 

generally lower in the spring, summer, and fall. On the other hand, during other seasons 

ambient levels of ozone and aeroallergens are more likely to be elevated, which could also 

increase the risk of ER visits for asthma. We are currently examining the relationships of 

PM10 and other ambient pollutants to respiratory and cardiovascular morbidity in the Bay

Area throughout the year. 

Our results are consistent with numerous other recent reports linking airborne particles to 

adverse respiratory outcomes when measured outdoor concentrations are lower than the 

federal ambient air quality standard for PM10 (33). To the extent that the associations noted 

in this report represent causal relationships, it is plausible that the heterogeneous categories 

of substances subsumed by PM10 (or fine particles, which represent the bulk of RWCrelated particles and which also are capable of substantial penetration to the indoor 

environment) may be responsible. In this study of winter air pollution and asthma, 

however, airborne particles represent but one of many respiratory irritants in wood smoke, 

including formaldehyde, acrolein, acetaldehyde, acetic acid, phenol, and nitrogen oxides, 

among others (3). Though the mean contribution of wood smoke to particle mass was 

reported to be approximately 45%, in some of the narrow inland valleys in Santa Clara 

County the percentage is likely to have been substantially higher. Thus, though PM10 is a 

routinely monitored indicator for wood smoke, the respiratory toxicity of the mixture, 

rather than particles per se, may have driven the relationships reported here. On the other 

hand, since approximately half of the winter particle mass during this study period was 

attributable to sources other than RWC, one cannot directly link the elevated RRs observed 

here exclusively to the latter. 

Unlike most other human activities that generate air pollution, RWC remains largely 

unregulated, despite substantial contributions to atmospheric particle loading in many areas 

of North America, especially in the western United States. It is also a major source of 

indoor pollution in many developing countries. Though wood combustion is probably the 

oldest form of anthropogenic pollution, there is a paucity of data on health impacts of 

smoke inhalation except at high concentrations. Future research should address effects of 

ambient levels of wood smoke on sensitive subpopulations (i.e., asthmatics) in chamber 

studies and in epidemiologic investigations in areas where RWC is the primary source of 

air pollution. 

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